Babita Majhi

IIR system identification using cat swarm optimization

Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification.

Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques

The present paper introduces the use of BFO and ABFO techniques to develop an efficient forecasting model for prediction of various stock indices. The structure used in these forecasting models is a simple linear combiner. The connecting weights of the adaptive linear combiner based models are optimized using ABFO and BFO by minimizing its mean square error (MSE). The short and long term prediction performance of these models are evaluated with test data and the results obtained are compared with those obtained from the genetic algorithm (GA) and particle swarm optimization (PSO) based models. It is in general observed that the new models are computationally more efficient, prediction wise more accurate and show faster convergence compared to other evolutionary computing models such as GA and PSO based models.

Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique

The paper introduces a novel method of adaptive robust identification of complex nonlinear dynamic plants including Box Jenkin, Mackey Glass and Sunspot series under the presence of strong outliers in the training samples. The identification model consists of a low complexity single layer functional link artificial neural network (FLANN) in the feed forward path and another on the feedback path. The connecting weights are iteratively adjusted by a population based particle swarm optimization technique so that a robust cost function (RCF) of the model-error is minimized. To demonstrate robust identification performance up to 50% random samples of the plant output is contaminated with strong outliers and are employed for training the model using PSO tool. Identification of wide varieties of benchmark complex static and dynamic plants is carried out through simulation study and the performance obtained are compared with those obtained from using standard squared error norm as CF. It is in general observed that, the Wilcoxon norm provides best identification performance compared to squared error and other RCFs based models.

Identification of nonlinear systems using particle swarm optimization technique

System identification in noisy environment has been a matter of concern for researchers in many disciplines of science and engineering. In the past the least mean square algorithm (LMS), genetic algorithm (GA) etc. have been employed for developing a parallel model. During training by LMS algorithm the weights rattle around and does not converge to optimal solution. This gives rise to poor performance of the model. Although GA always ensures the convergence of the weights to the global optimum but it suffers from slower convergence rate. To alleviate the problem we propose a novel Particle Swarm Optimization (PSO) technique for identifying nonlinear systems. The PSO is also a population based derivative free optimization technique like GA, and hence ascertains the convergence of the model parameters to the global optimum, there by yielding the same performance as provided by GA but with a faster speed. Comprehensive computer simulations validate that the PSO based identification is a better candidate even under noisy condition both in terms of convergence speed as well as number of input samples used.

Improved identification of Hammerstein plants using new CPSO and IPSO algorithms

Identification of Hammerstein plants finds extensive applications in stability analysis and control design. For identification of such complex plants, the recent trend of research is to employ nonlinear network and to train their weights by evolutionary computing tools. In recent years the area of Artificial Immune System (AIS) has drawn attention of many researchers due to its broad applicability to different fields. In this paper by combining the principles of AIS and PSO, we propose two new but simple hybrid algorithms called Clonal PSO (CPSO) and Immunized PSO (IPSO) which involve less complexity and offers better identification performance. Identification of few benchmark Hammerstein models is carried out through simulation study and the results obtained are compared with those obtained by standard PSO, Clonal and GA based methods. Various simulation results demonstrate that IPSO algorithm offers best identification performance compared to the other algorithms. Out of the two algorithms proposed, the CPSO is computationally simpler but offers identification performance nearly similar to its PSO counterpart.

Development of efficient identification scheme for nonlinear dynamic systems using swarm intelligence techniques

This paper outlines the basic concept and principles of two simple and powerful swarm intelligence tools: the particle swarm optimization (PSO) and the Bacterial Foraging Optimization (BFO). The adaptive identification of an unknown plant has been formulated as an optimization problem and then solved using the PSO and BFO techniques. Using this new approach efficient identification of complex nonlinear dynamic plants have been carried out through simulation study.

Bacterial foraging based identification of nonlinear dynamic system

Identification of nonlinear dynamic system plays an important role in many applications such as control engineering, telecommunication and intelligent instrumentation. The present paper investigates on the use of Bacterial Foraging in identification of nonlinear dynamic systems employing an efficient Functional link artificial neural network (FLANN) model. The BFO is a derivative free optimization tool and hence does not permit the solution of connecting weights to fall in local minima. This potential tool is employed in the paper to update the weights of the FLANN model. To assess the performance of the new model simulation studies of both the BFO-FLANN and multilayered ANN (MLANN) identification models are carried out. These experiments reveal that the two models exhibit identical identification performance. But, the proposed model offers low computational complexity and achieves faster convergence compared to its MLANN counterpart.

On the Development of a New Adaptive Channel Equalizer using Bacterial Foraging Optimization Technique

High speed data transmission over communication channels distort the transmitted signals in both amplitude and phase due to presence of inter symbol interference (ISI). Other impairments like thermal noise, impulse noise and cross talk also cause further distortions to the received symbols. Adaptive equalization of the digital channels at the receiver removes/reduces the effects of such ISIs and attempts to recover the transmitted symbols. The multilayer perceptron (MLP), fuzzy logic (FL) and radial basis function (RBF) based equalizers are relatively new soft computing based equalizers which aim to minimize the ISI present in the channels particularly for nonlinear channels. However they suffer from long training time and undesirable local minima. In the present paper we propose a new adaptive channel equalizer using a novel bacterial foraging optimization (BFO) technique which is essentially a derivative free optimization tool. This algorithm has been suitably used to update the weights of the equalizer. The performance of the proposed equalizer has been evaluated and has been compared with its LMS based counter part. It is observed that the new equalizer offers improved performance both in terms of rate of convergence as well as bit-error-rate

Multiobjective optimization based adaptive models with fuzzy decision making for stock market forecasting

Stock market forecasting is an important and challenging task. Conventional single objective optimization based adaptive prediction models reported in the literature do not satisfy many cost functions simultaneously. Very few reported materials are available on the development of multiobjective optimization based stock market prediction models. In this paper multiobjective particle swarm optimization (MOPSO) and nondominated sorting genetic algorithm version-II (NSGA-II) have been introduced to effectively train the adaptive stock market prediction models which simultaneously optimize four performance measures. The model developed is an adaptive one with nonlinearity introduced at the input end by Legendre polynomial expansion scheme. The stepwise algorithms are provided to develop the model and simulation study is carried to evaluate the performance. To arrive at the best possible solutions from these models, fuzzy logic based decision making strategy is suggested. Close examination of simulation results reveals that in terms of directional accuracy (DA) and computation time MOPSO based model is better where as in terms of average relative variance (ARV) and I-metric the NSGA-II model is superior. However, with regard to mean average percentage of error (MAPE) and Thelis U, MOPSO is better above one month ahead prediction. But for below one month ahead prediction, the NSGA-II model is preferable. To facilitate comparison two single objective optimization based models (PSO and GA based) are also developed and the performance has been obtained through simulation study. Comparison of the results demonstrate that in terms of MAPE and DA the performance of multiobjective is better where as the single objective optimization model exhibit superior performance in terms of Thelis U and the ARV.

Efficient scheme of pole-zero system identification using Particle Swarm Optimization technique

This paper introduces the application of particle swarm optimization (PSO) technique to identify the parameters of pole-zero plants or infinite impulse response (IIR) systems. The PSO is one of the evolutionary computing tools that performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge to a suitable solution with low computational complexity. This paper applies this powerful PSO tool to identify the parameters of standard IIR systems and compares the results with those obtained using the genetic algorithm (GA). The comparative results reveal that the PSO shows faster convergence, involves low complexity, yields minimum MSE level and exhibits superior identification performance in comparison to its GA counterpart.